Detecting the Direction of Information Flow in Instantaneous Relations Between Variables
Bibliographic record
Abstract
Data-based causality analysis tries to detect the true structural relations between measurements of complex multivariate systems. The detected relations should correspond to the true structure of the underlying data generation process. Even though there are many methodologies developed to extract causal relations from data, existence of instantaneous correlation between some variables in the data set, requires special care in order to correctly do the analysis. It is required to detect the instantaneous relations between variables as a prerequisite for subsequent causality analysis. Not only is detection of instantaneous relations important, but it is also necessary to discover the direction of information flow in the instantaneous relations. This piece of information plays a vital role in selection of correct modeling structure to achieve a reliable result about causal relations between variables. Using prior knowledge about the process or blind mathematical transformations are usual solutions for this problem in the literature. However, there is a lack of reliable mathematical methodologies to address this issue completely based on data analysis. This brief proposes a method to detect the direction of instantaneous causal relations between variables and supports it through simulation and case studies. The proposed algorithm uses a third variable as an instrument to detect the direction of information flow between any two instantaneously correlated variables. The instrument variable is required to meet some conditions for the algorithm to work; however, the application of the algorithm does not require any prior information about the process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".